import argparse

import mmcv
from mmcv import Config, DictAction
from mmcv.parallel import MMDataParallel

from mmdet.apis import single_gpu_test
from mmdet.core.export.model_wrappers import ONNXRuntimeDetector
from mmdet.datasets import build_dataloader, build_dataset, replace_ImageToTensor


def parse_args():
    parser = argparse.ArgumentParser(
        description="MMDet test (and eval) an ONNX model using ONNXRuntime"
    )
    parser.add_argument("config", help="test config file path")
    parser.add_argument("model", help="Input model file")
    parser.add_argument("--out", help="output result file in pickle format")
    parser.add_argument(
        "--format-only",
        action="store_true",
        help="Format the output results without perform evaluation. It is"
        "useful when you want to format the result to a specific format and "
        "submit it to the test server",
    )
    parser.add_argument(
        "--eval",
        type=str,
        nargs="+",
        help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
        ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC',
    )
    parser.add_argument("--show", action="store_true", help="show results")
    parser.add_argument(
        "--show-dir", help="directory where painted images will be saved"
    )
    parser.add_argument(
        "--show-score-thr",
        type=float,
        default=0.3,
        help="score threshold (default: 0.3)",
    )
    parser.add_argument(
        "--cfg-options",
        nargs="+",
        action=DictAction,
        help="override some settings in the used config, the key-value pair "
        "in xxx=yyy format will be merged into config file. If the value to "
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        "Note that the quotation marks are necessary and that no white space "
        "is allowed.",
    )
    parser.add_argument(
        "--eval-options",
        nargs="+",
        action=DictAction,
        help="custom options for evaluation, the key-value pair in xxx=yyy "
        "format will be kwargs for dataset.evaluate() function",
    )

    args = parser.parse_args()
    return args


def main():
    args = parse_args()

    assert args.out or args.eval or args.format_only or args.show or args.show_dir, (
        "Please specify at least one operation (save/eval/format/show the "
        'results / save the results) with the argument "--out", "--eval"'
        ', "--format-only", "--show" or "--show-dir"'
    )

    if args.eval and args.format_only:
        raise ValueError("--eval and --format_only cannot be both specified")

    if args.out is not None and not args.out.endswith((".pkl", ".pickle")):
        raise ValueError("The output file must be a pkl file.")

    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)

    # in case the test dataset is concatenated
    samples_per_gpu = 1
    if isinstance(cfg.data.test, dict):
        cfg.data.test.test_mode = True
        samples_per_gpu = cfg.data.test.pop("samples_per_gpu", 1)
        if samples_per_gpu > 1:
            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
            cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
    elif isinstance(cfg.data.test, list):
        for ds_cfg in cfg.data.test:
            ds_cfg.test_mode = True
        samples_per_gpu = max(
            [ds_cfg.pop("samples_per_gpu", 1) for ds_cfg in cfg.data.test]
        )
        if samples_per_gpu > 1:
            for ds_cfg in cfg.data.test:
                ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)

    # build the dataloader
    dataset = build_dataset(cfg.data.test)
    data_loader = build_dataloader(
        dataset,
        samples_per_gpu=samples_per_gpu,
        workers_per_gpu=cfg.data.workers_per_gpu,
        dist=False,
        shuffle=False,
    )

    model = ONNXRuntimeDetector(args.model, class_names=dataset.CLASSES, device_id=0)

    model = MMDataParallel(model, device_ids=[0])
    outputs = single_gpu_test(
        model, data_loader, args.show, args.show_dir, args.show_score_thr
    )

    if args.out:
        print(f"\nwriting results to {args.out}")
        mmcv.dump(outputs, args.out)
    kwargs = {} if args.eval_options is None else args.eval_options
    if args.format_only:
        dataset.format_results(outputs, **kwargs)
    if args.eval:
        eval_kwargs = cfg.get("evaluation", {}).copy()
        # hard-code way to remove EvalHook args
        for key in ["interval", "tmpdir", "start", "gpu_collect", "save_best", "rule"]:
            eval_kwargs.pop(key, None)
        eval_kwargs.update(dict(metric=args.eval, **kwargs))
        print(dataset.evaluate(outputs, **eval_kwargs))


if __name__ == "__main__":
    main()
